1. Ratain MJ, Eckhardt SG. Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. J Clin Oncol. 2004. 22:4442–4445.
2. Rosner GL, Stadler W, Ratain MJ. Randomized discontinuation design: application to cytostatic antineoplastic agents. J Clin Oncol. 2002. 20:4478–4484.
3. Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer. 1981. 47:207–214.
4. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000. 92:205–216.
5. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009. 45:228–247.
6. van Persijn van Meerten EL, Gelderblom H, Bloem JL. RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline. Eur Radiol. 2010. 20:1456–1467.
7. Scher HI, Morris MJ, Kelly WK, Schwartz LH, Heller G. Prostate cancer clinical trial end points: "RECIST"ing a step backwards. Clin Cancer Res. 2005. 11:5223–5232.
8. Nowak AK. CT, RECIST, and malignant pleural mesothelioma. Lung Cancer. 2005. 49:Suppl 1. S37–S40.
9. Byrne MJ, Nowak AK. Modified RECIST criteria for assessment of response in malignant pleural mesothelioma. Ann Oncol. 2004. 15:257–260.
10. van Klaveren RJ, Aerts JG, de Bruin H, Giaccone G, Manegold C, van Meerbeeck JP. Inadequacy of the RECIST criteria for response evaluation in patients with malignant pleural mesothelioma. Lung Cancer. 2004. 43:63–69.
11. Therasse P, Eisenhauer EA, Verweij J. RECIST revisited: a review of validation studies on tumour assessment. Eur J Cancer. 2006. 42:1031–1039.
12. Gopinath G, Ahmed A, Buscombe JR, Dickson JC, Caplin ME, Hilson AJ. Prediction of clinical outcome in treated neuroendocrine tumours of carcinoid type using functional volumes on 111In-pentetreotide SPECT imaging. Nucl Med Commun. 2004. 25:253–257.
13. Barnacle AM, McHugh K. Limitations with the response evaluation criteria in solid tumors (RECIST) guidance in disseminated pediatric malignancy. Pediatr Blood Cancer. 2006. 46:127–134.
14. Bruix J, Sherman M, Llovet JM, Beaugrand M, Lencioni R, Burroughs AK, et al. Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver. J Hepatol. 2001. 35:421–430.
15. Varela M, Real MI, Burrel M, Forner A, Sala M, Brunet M, et al. Chemoembolization of hepatocellular carcinoma with drug eluting beads: efficacy and doxorubicin pharmacokinetics. J Hepatol. 2007. 46:474–481.
16. Lencioni R, Cioni D, Crocetti L, Franchini C, Pina CD, Lera J, et al. Early-stage hepatocellular carcinoma in patients with cirrhosis: long-term results of percutaneous image-guided radiofrequency ablation. Radiology. 2005. 234:961–967.
17. Sala M, Llovet JM, Vilana R, Bianchi L, Solé M, Ayuso C, et al. Initial response to percutaneous ablation predicts survival in patients with hepatocellular carcinoma. Hepatology. 2004. 40:1352–1360.
18. Llovet JM, Real MI, Montaña X, Planas R, Coll S, Aponte J, et al. Arterial embolisation or chemoembolisation versus symptomatic treatment in patients with unresectable hepatocellular carcinoma: a randomised controlled trial. Lancet. 2002. 359:1734–1739.
19. Riaz A, Kulik L, Lewandowski RJ, Ryu RK, Giakoumis Spear G, Mulcahy MF, et al. Radiologic-pathologic correlation of hepatocellular carcinoma treated with internal radiation using yttrium-90 microspheres. Hepatology. 2009. 49:1185–1193.
20. Forner A, Ayuso C, Varela M, Rimola J, Hessheimer AJ, de Lope CR, et al. Evaluation of tumor response after locoregional therapies in hepatocellular carcinoma: are response evaluation criteria in solid tumors reliable? Cancer. 2009. 115:616–623.
21. Sleijfer S, Wiemer E, Verweij J. Drug Insight: gastrointestinal stromal tumors (GIST)--the solid tumor model for cancer-specific treatment. Nat Clin Pract Oncol. 2008. 5:102–111.
22. Choi H, Charnsangavej C, de Castro Faria S, Tamm EP, Benjamin RS, Johnson MM, et al. CT evaluation of the response of gastrointestinal stromal tumors after imatinib mesylate treatment: a quantitative analysis correlated with FDG PET findings. AJR Am J Roentgenol. 2004. 183:1619–1628.
23. Choi H, Charnsangavej C, Faria SC, Macapinlac HA, Burgess MA, Patel SR, et al. Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol. 2007. 25:1753–1759.
24. Choi H. Response evaluation of gastrointestinal stromal tumors. Oncologist. 2008. 13:Suppl 2. 4–7.
25. Chun YS, Vauthey JN, Boonsirikamchai P, Maru DM, Kopetz S, Palavecino M, et al. Association of computed tomography morphologic criteria with pathologic response and survival in patients treated with bevacizumab for colorectal liver metastases. JAMA. 2009. 302:2338–2344.
26. van der Veldt AA, Meijerink MR, van den Eertwegh AJ, Haanen JB, Boven E. Choi response criteria for early prediction of clinical outcome in patients with metastatic renal cell cancer treated with sunitinib. Br J Cancer. 2010. 102:803–809.
27. Smith AD, Lieber ML, Shah SN. Assessing tumor response and detecting recurrence in metastatic renal cell carcinoma on targeted therapy: importance of size and attenuation on contrast-enhanced CT. AJR Am J Roentgenol. 2010. 194:157–165.
28. Smith AD, Shah SN, Rini BI, Lieber ML, Remer EM. Morphology, Attenuation, Size, and Structure (MASS) criteria: assessing response and predicting clinical outcome in metastatic renal cell carcinoma on antiangiogenic targeted therapy. AJR Am J Roentgenol. 2010. 194:1470–1478.
29. Sandler AB, Schiller JH, Gray R, Dimery I, Brahmer J, Samant M, et al. Retrospective evaluation of the clinical and radiographic risk factors associated with severe pulmonary hemorrhage in first-line advanced, unresectable non-small-cell lung cancer treated with Carboplatin and Paclitaxel plus bevacizumab. J Clin Oncol. 2009. 27:1405–1412.
30. Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology. 2011. 261:165–171.
31. Lee HY, Lee KS, Ahn MJ, Hwang HS, Lee JW, Park K, et al. New CT response criteria in non-small cell lung cancer: proposal and application in EGFR tyrosine kinase inhibitor therapy. Lung Cancer. 2011. 73:63–69.
32. Lee HY, Lee KS, Hwang HS, Lee JW, Ahn MJ, Park K, et al. Molecularly targeted therapy using bevacizumab for non-small cell lung cancer: a pilot study for the new CT response criteria. Korean J Radiol. 2010. 11:618–626.
33. Hamaoka T, Costelloe CM, Madewell JE, Liu P, Berry DA, Islam R, et al. Tumour response interpretation with new tumour response criteria vs the World Health Organisation criteria in patients with bone-only metastatic breast cancer. Br J Cancer. 2010. 102:651–657.
34. Coleman RE, Mashiter G, Whitaker KB, Moss DW, Rubens RD, Fogelman I. Bone scan flare predicts successful systemic therapy for bone metastases. J Nucl Med. 1988. 29:1354–1359.
35. Imbriaco M, Larson SM, Yeung HW, Mawlawi OR, Erdi Y, Venkatraman ES, et al. A new parameter for measuring metastatic bone involvement by prostate cancer: the Bone Scan Index. Clin Cancer Res. 1998. 4:1765–1772.
36. Dennis ER, Jia X, Mezheritskiy IS, Stephenson RD, Schoder H, Fox JJ, et al. Bone scan index: a quantitative treatment response biomarker for castration-resistant metastatic prostate cancer. J Clin Oncol. 2012. 30:519–524.
37. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med. 2009. 50:Suppl 1. 122S–150S.
38. Kawashima A, Libshitz HI. Malignant pleural mesothelioma: CT manifestations in 50 cases. AJR Am J Roentgenol. 1990. 155:965–969.
39. Armato SG 3rd, Ogarek JL, Starkey A, Vogelzang NJ, Kindler HL, Kocherginsky M, et al. Variability in mesothelioma tumor response classification. AJR Am J Roentgenol. 2006. 186:1000–1006.
40. Lee HY, Hyun SH, Lee KS, Kim BT, Kim J, Shim YM, et al. Volume-based parameter of 18)F-FDG PET/CT in malignant pleural mesothelioma: prediction of therapeutic response and prognostic implications. Ann Surg Oncol. 2010. 17:2787–2794.
41. Fischer BM, Mortensen J, Højgaard L. Positron emission tomography in the diagnosis and staging of lung cancer: a systematic, quantitative review. Lancet Oncol. 2001. 2:659–666.
42. Hoekstra CJ, Stroobants SG, Hoekstra OS, Vansteenkiste J, Biesma B, Schramel FJ, et al. The value of [18F]fluoro-2-deoxy-D-glucose positron emission tomography in the selection of patients with stage IIIA-N2 non-small cell lung cancer for combined modality treatment. Lung Cancer. 2003. 39:151–157.
43. Berghmans T, Dusart M, Paesmans M, Hossein-Foucher C, Buvat I, Castaigne C, et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol. 2008. 3:6–12.
44. Kramer H, Post WJ, Pruim J, Groen HJ. The prognostic value of positron emission tomography in non-small cell lung cancer: analysis of 266 cases. Lung Cancer. 2006. 52:213–217.
45. Hoekstra CJ, Stroobants SG, Smit EF, Vansteenkiste J, van Tinteren H, Postmus PE, et al. Prognostic relevance of response evaluation using [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography in patients with locally advanced non-small-cell lung cancer. J Clin Oncol. 2005. 23:8362–8370.
46. Lee DH, Kim SK, Lee HY, Lee SY, Park SH, Kim HY, et al. Early prediction of response to first-line therapy using integrated 18F-FDG PET/CT for patients with advanced/metastatic non-small cell lung cancer. J Thorac Oncol. 2009. 4:816–821.
47. Decoster L, Schallier D, Everaert H, Nieboer K, Meysman M, Neyns B, et al. Complete metabolic tumour response, assessed by 18-fluorodeoxyglucose positron emission tomography (18FDG-PET), after induction chemotherapy predicts a favourable outcome in patients with locally advanced non-small cell lung cancer (NSCLC). Lung Cancer. 2008. 62:55–61.
48. Sunaga N, Oriuchi N, Kaira K, Yanagitani N, Tomizawa Y, Hisada T, et al. Usefulness of FDG-PET for early prediction of the response to gefitinib in non-small cell lung cancer. Lung Cancer. 2008. 59:203–210.
49. Aukema TS, Kappers I, Olmos RA, Codrington HE, van Tinteren H, van Pel R, et al. Is 18F-FDG PET/CT useful for the early prediction of histopathologic response to neoadjuvant erlotinib in patients with non-small cell lung cancer? J Nucl Med. 2010. 51:1344–1348.
50. Su H, Bodenstein C, Dumont RA, Seimbille Y, Dubinett S, Phelps ME, et al. Monitoring tumor glucose utilization by positron emission tomography for the prediction of treatment response to epidermal growth factor receptor kinase inhibitors. Clin Cancer Res. 2006. 12:5659–5667.
51. McDermott GM, Welch A, Staff RT, Gilbert FJ, Schweiger L, Semple SI, et al. Monitoring primary breast cancer throughout chemotherapy using FDG-PET. Breast Cancer Res Treat. 2007. 102:75–84.
52. Semple SI, Gilbert FJ, Redpath TW, Staff RT, Ahearn TS, Welch AE, et al. The relationship between vascular and metabolic characteristics of primary breast tumours. Eur Radiol. 2004. 14:2038–2045.
53. Semple SI, Staff RT, Heys SD, Redpath TW, Welch AE, Ahearn TS, et al. Baseline MRI delivery characteristics predict change in invasive ductal breast carcinoma PET metabolism as a result of primary chemotherapy administration. Ann Oncol. 2006. 17:1393–1398.
54. Goh V, Ng QS, Miles K. Computed tomography perfusion imaging for therapeutic assessment: has it come of age as a biomarker in oncology? Invest Radiol. 2012. 47:2–4.
55. Jiang T, Kambadakone A, Kulkarni NM, Zhu AX, Sahani DV. Monitoring response to antiangiogenic treatment and predicting outcomes in advanced hepatocellular carcinoma using image biomarkers, CT perfusion, tumor density, and tumor size (RECIST). Invest Radiol. 2012. 47:11–17.
56. Reiner CS, Goetti R, Eberli D, Klotz E, Boss A, Pfammatter T, et al. CT perfusion of renal cell carcinoma: impact of volume coverage on quantitative analysis. Invest Radiol. 2012. 47:33–40.
57. Padhani AR, Khan AA. Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy. Target Oncol. 2010. 5:39–52.
58. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999. 10:223–232.
59. Barentsz JO, Berger-Hartog O, Witjes JA, Hulsbergen-van der Kaa C, Oosterhof GO, VanderLaak JA, et al. Evaluation of chemotherapy in advanced urinary bladder cancer with fast dynamic contrast-enhanced MR imaging. Radiology. 1998. 207:791–797.
60. Ah-See ML, Makris A, Taylor NJ, Harrison M, Richman PI, Burcombe RJ, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res. 2008. 14:6580–6589.
61. Padhani AR, Hayes C, Assersohn L, Powles T, Makris A, Suckling J, et al. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results. Radiology. 2006. 239:361–374.
62. Reddick WE, Taylor JS, Fletcher BD. Dynamic MR imaging (DEMRI) of microcirculation in bone sarcoma. J Magn Reson Imaging. 1999. 10:277–285.
63. Padhani AR, Leach MO. Antivascular cancer treatments: functional assessments by dynamic contrast-enhanced magnetic resonance imaging. Abdom Imaging. 2005. 30:324–341.
64. Rosen MA, Schnall MD. Dynamic contrast-enhanced magnetic resonance imaging for assessing tumor vascularity and vascular effects of targeted therapies in renal cell carcinoma. Clin Cancer Res. 2007. 13(2 Pt 2):770s–776s.
65. Liu G, Rugo HS, Wilding G, McShane TM, Evelhoch JL, Ng C, et al. Dynamic contrast-enhanced magnetic resonance imaging as a pharmacodynamic measure of response after acute dosing of AG-013736, an oral angiogenesis inhibitor, in patients with advanced solid tumors: results from a phase I study. J Clin Oncol. 2005. 23:5464–5473.
66. Wedam SB, Low JA, Yang SX, Chow CK, Choyke P, Danforth D, et al. Antiangiogenic and antitumor effects of bevacizumab in patients with inflammatory and locally advanced breast cancer. J Clin Oncol. 2006. 24:769–777.
67. Chae EJ, Song JW, Seo JB, Krauss B, Jang YM, Song KS. Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology. 2008. 249:671–681.
68. Schmid-Bindert G, Henzler T, Chu TQ, Meyer M, Nance JW Jr, Schoepf UJ, et al. Functional imaging of lung cancer using dual energy CT: how does iodine related attenuation correlate with standardized uptake value of 18FDG-PET-CT? Eur Radiol. 2012. 22:93–103.
69. Kim YN, Lee HY, Lee KS, Seo JB, Chung MJ, Ahn MJ, et al. Dual-energy CT in patients treated with anti-angiogenic agents for non-small cell lung cancer: a new method of monitoring tumor response? Korean J Radiol. In press.
70. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol. 2007. 188:1622–1635.
71. Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, Corona-Villalobos CP, Kamel IR, et al. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics. 2011. 31:1773–1791.
72. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009. 11:102–125.
73. Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000. 92:2029–2036.
74. Dzik-Jurasz A, Domenig C, George M, Wolber J, Padhani A, Brown G, et al. Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet. 2002. 360:307–308.
75. DeVries AF, Kremser C, Hein PA, Griebel J, Krezcy A, Ofner D, et al. Tumor microcirculation and diffusion predict therapy outcome for primary rectal carcinoma. Int J Radiat Oncol Biol Phys. 2003. 56:958–965.
76. Koh DM, Scurr E, Collins D, Kanber B, Norman A, Leach MO, et al. Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol. 2007. 188:1001–1008.
77. Hein PA, Kremser C, Judmaier W, Griebel J, Pfeiffer KP, Kreczy A, et al. Diffusion-weighted magnetic resonance imaging for monitoring diffusion changes in rectal carcinoma during combined, preoperative chemoradiation: preliminary results of a prospective study. Eur J Radiol. 2003. 45:214–222.
78. Pickles MD, Gibbs P, Lowry M, Turnbull LW. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging. 2006. 24:843–847.
79. Sharma U, Danishad KK, Seenu V, Jagannathan NR. Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed. 2009. 22:104–113.
80. Chinnaiyan AM, Prasad U, Shankar S, Hamstra DA, Shanaiah M, Chenevert TL, et al. Combined effect of tumor necrosis factor-related apoptosis-inducing ligand and ionizing radiation in breast cancer therapy. Proc Natl Acad Sci U S A. 2000. 97:1754–1759.
81. Uhl M, Saueressig U, Koehler G, Kontny U, Niemeyer C, Reichardt W, et al. Evaluation of tumour necrosis during chemotherapy with diffusion-weighted MR imaging: preliminary results in osteosarcomas. Pediatr Radiol. 2006. 36:1306–1311.
82. Uhl M, Altehoefer C, Kontny U, Il'yasov K, Büchert M, Langer M. MRI-diffusion imaging of neuroblastomas: first results and correlation to histology. Eur Radiol. 2002. 12:2335–2338.
83. Yabuuchi H, Hatakenaka M, Takayama K, Matsuo Y, Sunami S, Kamitani T, et al. Non-small cell lung cancer: detection of early response to chemotherapy by using contrast-enhanced dynamic and diffusion-weighted MR imaging. Radiology. 2011. 261:598–604.
84. Park SY, Kim CK, Park BK, Lee HM, Lee KS. Prediction of biochemical recurrence following radical prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: initial results. Eur Radiol. 2011. 21:1111–1118.
85. Harry VN, Semple SI, Parkin DE, Gilbert FJ. Use of new imaging techniques to predict tumour response to therapy. Lancet Oncol. 2010. 11:92–102.
86. Manton DJ, Chaturvedi A, Hubbard A, Lind MJ, Lowry M, Maraveyas A, et al. Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy. Br J Cancer. 2006. 94:427–435.
87. Morse DL, Carroll D, Day S, Gray H, Sadarangani P, Murthi S, et al. Characterization of breast cancers and therapy response by MRS and quantitative gene expression profiling in the choline pathway. NMR Biomed. 2009. 22:114–127.
88. Baek HM, Chen JH, Nie K, Yu HJ, Bahri S, Mehta RS, et al. Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy. Radiology. 2009. 251:653–662.
89. Wu B, Peng WJ, Wang PJ, Gu YJ, Li WT, Zhou LP, et al. In vivo 1H magnetic resonance spectroscopy in evaluation of hepatocellular carcinoma and its early response to transcatheter arterial chemoembolization. Chin Med Sci J. 2006. 21:258–264.
90. Preul MC, Caramanos Z, Villemure JG, Shenouda G, LeBlanc R, Langleben A, et al. Using proton magnetic resonance spectroscopic imaging to predict in vivo the response of recurrent malignant gliomas to tamoxifen chemotherapy. Neurosurgery. 2000. 46:306–318.
91. Lodi A, Ronen SM. Magnetic resonance spectroscopy detectable metabolomic fingerprint of response to antineoplastic treatment. PLoS One. 2011. 6:e26155.
92. Padhani AR, Miles KA. Multiparametric imaging of tumor response to therapy. Radiology. 2010. 256:348–364.
93. Zhao B, James LP, Moskowitz CS, Guo P, Ginsberg MS, Lefkowitz RA, et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology. 2009. 252:263–272.
94. Oxnard GR, Zhao B, Sima CS, Ginsberg MS, James LP, Lefkowitz RA, et al. Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes. J Clin Oncol. 2011. 29:3114–3119.
95. Goldmacher GV, Conklin J. The use of tumour volumetrics to assess response to therapy in anticancer clinical trials. Br J Clin Pharmacol. 2012. 73:846–854.
96. Gavrielides MA, Kinnard LM, Myers KJ, Petrick N. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology. 2009. 251:26–37.
97. Zhao B, Schwartz LH, Moskowitz CS, Ginsberg MS, Rizvi NA, Kris MG. Lung cancer: computerized quantification of tumor response--initial results. Radiology. 2006. 241:892–898.
98. Food and Drug Administration. United States food and drug administration guidance for industry: clinical trial endpoints for the approval of cancer drugs and biologics. 2007. Rockville, MD: US Department of Health and Human Services.
99. Ford R, Schwartz L, Dancey J, Dodd LE, Eisenhauer EA, Gwyther S, et al. Lessons learned from independent central review. Eur J Cancer. 2009. 45:268–274.
100. Gwyther SJ, Schwartz LH. How to assess anti-tumour efficacy by imaging techniques. Eur J Cancer. 2008. 44:39–45.
101. Saini S. Radiologic measurement of tumor size in clinical trials: past, present, and future. AJR Am J Roentgenol. 2001. 176:333–334.
102. Sullivan DC, Gatsonis C. Response to treatment series: part 1 and introduction, measuring tumor response--challenges in the era of molecular medicine. AJR Am J Roentgenol. 2011. 197:15–17.
103. Hirsch FR, Mok TS, Borges V, Bunn PA Jr. Molecularly targeted therapy: when to stop and when to continue? Lancet Oncol. 2010. 11:709–711.